Below I will be publishing the source code for our ML.NET Neural Networks for the following stock indicators. Each will include a Rules Engines that dramatically speeds up the training of these networks. In the code below that I will be publishing on our GitHub site for the Indicators below we add a very different kind of Rules Engine for making money that dramatically shortens the time to train the neworks below as well as dramatically reducing teh amount of data need to train the network.

Pull Ratio (Gross divided by Media Cost):

This is an indicaator you raely see in stock trading but I have written many C++ Black Boxes for stock traaders who were always amazed when I demonstrated what this indicator can do. It is used to analize how much to BID, that's right BID, on acquiring media of any type. Only uneducated fools would ever pay the rate for any advertsing. In terms of television and radio time it is defined in federal law as a"negotiated price product" and "Rate cards" are only suggested prices. The same is true for all newspaaper and magazine advertising. The neural netwrk for Pull Ratio allows us to calulate how much to bid to guarantee a net rofit on any media buy. This is how I made my fortune in television marketing as well as newspaper and magazine advertising.

Earnings Per Share (EPS):

Adding a neural network to the analysis of EPS can enhance the indicator by identifying hidden patterns in a company’s historical earnings and its correlations with external factors like industry trends, macroeconomic data, and competitor performance. The neural network can predict future EPS with high accuracy, enabling more informed investment decisions by forecasting growth potential beyond traditional linear methods.

Price-to-Earnings (P/E) Ratio:

A neural network can improve the P/E ratio's utility by incorporating dynamic market conditions, such as sector-specific trends, interest rates, and geopolitical events, to contextualize whether a stock is undervalued or overvalued. This predictive capacity enables real-time adjustments to valuation benchmarks, making the P/E ratio a more adaptive and reliable metric.

Price-to-Book (P/B) Ratio:

By integrating neural networks, the P/B ratio can evolve into a predictive tool that factors in changes in a company’s asset valuation, depreciation schedules, and industry-specific risks. Neural networks can analyze historical book values alongside market behavior to detect mispricings and potential reversals, enhancing the ratio's predictive accuracy.

Return on Equity (ROE):

Neural networks can enhance ROE by modeling non-linear relationships between profitability, leverage, and market conditions. This allows for forecasting future ROE trends under various scenarios, offering insights into how management decisions and external factors will likely affect shareholder returns.

Dividend Yield:

Incorporating a neural network into dividend yield analysis can refine the indicator by predicting dividend payout stability and growth based on cash flow trends, sector-specific data, and historical performance. It can also assess the sustainability of dividend yields in different economic environments, offering deeper insights into long-term returns.

Debt-to-Equity Ratio:

A neural network can transform the debt-to-equity ratio by modeling its impact on stock volatility and risk profiles. By analyzing historical data, credit market trends, and economic indicators, the network can provide probabilistic scenarios of financial distress or growth, enabling more nuanced interpretations of a company’s financial leverage.

Current Ratio:

Neural networks can enhance the current ratio’s predictive power by analyzing seasonal patterns in working capital, supply chain dynamics, and market trends. This allows investors to anticipate potential liquidity issues before they appear in traditional financial statements.

Moving Averages (MA):

Neural networks can optimize moving averages by dynamically adjusting the smoothing period based on market volatility and asset-specific patterns. This approach reduces lag and enhances trend detection, providing more accurate signals for buy and sell decisions.

Relative Strength Index (RSI):

A neural network can refine the RSI by learning complex patterns in market behavior and adapting thresholds for overbought and oversold conditions based on real-time data. This increases the accuracy of market entry and exit signals, reducing false positives in volatile markets.

Moving Average Convergence Divergence (MACD):

Integrating a neural network into MACD analysis allows for predictive insights into price momentum by learning historical crossovers and divergences under different market conditions. This predictive modeling improves the timing of trades and increases profitability.

Bollinger Bands:

Neural networks can enhance Bollinger Bands by dynamically adjusting the width of the bands based on volatility and predictive modeling of price fluctuations. This allows for better detection of breakouts or reversals, reducing the likelihood of being misled by false signals.

On-Balance Volume (OBV):

By using neural networks, OBV can integrate market sentiment data, such as social media trends or news sentiment, alongside volume and price trends. This creates a more nuanced understanding of the relationship between volume and price movements, improving predictions of future trends.

Stochastic Oscillator:

A neural network can improve the stochastic oscillator by analyzing historical oscillator movements and integrating additional variables like macroeconomic data or market sentiment. This helps in predicting more accurate turning points, enhancing the timing of trades.

Fibonacci Retracement Levels:

Neural networks can enhance Fibonacci retracements by identifying patterns in historical retracement levels and predicting future support and resistance levels more accurately. This makes the indicator more robust in dynamic and volatile markets, offering precise entry and exit points.